Author: C. Moyses Araujo
Title: Atomic-scale Modelling of Organic Electrode Materials
Affiliation: Uppsala University
The organic electrode materials (OEM) are emerging as a promising alternative to develop greener and sustainable battery technologies. However, the poor cycling stability and low energy densities hinders their fast implementation. To overcome these hurdles, it is essential to achieve a fundamental understanding at atomic-scale of the lithiation/delithiation processes. To contribute to this end, we are developing methodologies based on evolutionary algorithms (EA) and deep neural networks (DNN) at interplay with density functional theory (DFT). They EA has been employed to predict the structure and electrochemistry of a set of dicarboxylates while the DNN has been employed in a novel machine learning approach to obtain the redox potentials. A number of learning algorithms have been investigated along with different molecular representations based (e.g. the many-body tensor representation). This study provides a framework that can aid the designing of novel organic electrode materials.